Basic science discoveries and cancer treatment innovations have led to 15.5 million cancer survivors currently in the United States, and the number of survivors is estimated to reach 20 million by 2026. Cancer and its treatment often cause long-term effects, including both persistent symptoms and functional deficits that negatively impact survivors? quality of life. Cancer survivors are an ideal population in which to examine multiple co-occurring symptoms, termed ?symptom clusters.? A 2017 panel of experts in symptom science convened by the National Institute of Nursing Research (NINR) and the Office of Rare Diseases noted that patients with chronic conditions such as cancer experience an array of symptom clusters that have a negative impact on health-related quality of life (HRQOL). This panel called for advancing symptom science through symptom cluster research to build the evidence base for effective symptom management. Our proposal is in response to an NINR/NCI joint program announcement that emerged from this workshop. Most prior research on symptom clusters in cancer has focused on patients undergoing the acute phase of cancer treatment for specific common cancers often treated in academic referral centers. Our study will address these limitations by focusing on early stage, sociodemographically diverse adult cancer survivors treated in general US oncology practices. We will investigate symptom clusters using data from a previously surveyed cohort of 5,506 adult cancer survivors initially diagnosed between 21-84 years of age with 1 of 7 distinct cancer types who were recruited in partnership with 4 population-based Surveillance, Epidemiology, and Ends Results (SEER) cancer registries in 3 states (CA, NJ, LA). This cohort was first surveyed at a median of 9 months after initial cancer diagnosis with a follow-up survey conducted 6 months later. The SEER registry data include detailed clinical and treatment data, and the survey collected information on 8 domains of symptoms and functional status common and highly impactful in cancer survivors, and numerous other sociodemographic and economic variables. In Aim 1, we will identify the prevalence of symptom clusters and subgroup profiles using innovative psychometric and statistical methods. We will evaluate sociodemographic (e.g., age, sex, race-ethnicity, income, education) and clinical characteristics (e.g., cancer type, stage, treatment, and comorbidity) associated with each symptom cluster and survivor subgroup. In Aim 2 we will investigate the temporal patterns of symptom clusters using state-of-the-art latent transition analysis. In both aims, we will evaluate the role of several mutable factors at the patient and healthcare system levels to inform future interventional research. In Aim 3 we will investigate the association of evidence-based symptom and comorbidity care with the prevalence and trajectory of symptom clusters using linked Medicare claims data for the subgroup of survivors ages 65 and over. Our results will enable a richer understanding of the phenomenon of symptom clusters and will provide a new foundation of knowledge that will help inform future interventions to mitigate the adverse effects of symptom clusters.